Facing Threats to Libraries and Cultural Heritage in the Russia-Ukraine War: A Case Study and Comparative Review of the Library and Information Community’s’ Responses to the Conflict
Bibliographic record
Abstract
On 24 February 2022, Russia invaded and began a war in Ukraine. After it commenced, the international library and information community began responding. Specifically, formal public-facing response on the conflict were released by the American Library Association (ALA), Australian Library and Information Association (ALIA), Canadian Federation of Library Associations (CFLA), Chartered Institute of Library and Information Professionals (CILIP), Danish Library Association (DLA), European Bureau of Library, Information and Documentation Associations (EBLIDA), International Federation of Library Associations (IFLA), Library Association of Latvia (LAL), and New Zealand Library Association Inc. (LIANZA). Chronicling and describing the international library and information community’s first public-facing responses addressing Russia’s war in Ukraine is the main objective of this article. Drawing upon a combined policy and thematic analysis of some of these first formal public responses, the article aims are to help account, review, and contextualize the ways in which this community considered the war during its first week and, in turn, reveal areas or issues of convergence or divergence between them. Specifically, it provides a snapshot in time revealing the international library and information community’s immediate perspectives and positions on the war during its earliest stages. For instance, the formal public responses released by the ALA, ALIA, CFLA, CILIP, DLA, EBLIDA, IFLA, LAL, and LIANZA during the war’s first week demonstrates international concern about the conflict and its affects on their Ukrainian counterparts and cultural heritage. Broad thematic convergence surfaces across the responses. Almost all plead for solutions to and resolution of the war. A majority offer solidarity for Ukrainian colleagues and all Ukrainians, support democracy and freedom of expression, asseverate for spreading accurate information about the war, and condemn Russia’s assault. Additional themes appearing in some of the responses include assisting Ukrainian refugees and displaying dismay regarding threats confronting Ukrainian cultural heritage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.021 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".